Systems and methods for providing user insights based on heart rate variability

ABSTRACT

A system can include a wearable device that obtains real-time physiological data and activity data from a user and transmits that data to another device. A computing device can receive HRV and activity data and determine whether the user&#39;s autonomic nervous system is in a predominantly sympathetic or parasympathetic state. For example, the determination can include comparing an average variance in a portion of the HRV data with a threshold value. In response to determining that the user&#39;s autonomic nervous system is in a sympathetic state, the device can perform an action.

PRIORITY CLAIM

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/487,333, entitled “SYSTEMS AND METHODS FOR PROVIDING USERINSIGHTS BASED ON HEART RATE VARIABILITY” and filed Apr. 19, 2017, whichis hereby incorporated herein in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to systems and methods that usenon-invasive electro-optical technology for sensing, measuring, andinterpreting physiological parameters relating to a user's heart rate,and more specifically, to systems and methods for providing userinsights or taking actions based on a user's heart rate variability.

BACKGROUND

Many portable devices have been developed in which optical sensors areused to detect variation in blood flow through arteries or blood volumein subcutaneous tissue. Applications include the monitoring of heartrate, glucose level, apnea, respiratory stress, and other physiologicalconditions. The optical sensors often comprise one or more light sourcesthat illuminate a targeted portion of the human body and one or moreassociated optical detectors that receive a portion of the opticalenergy emitted by the light sources.

One area of growing interest in the use of physiological monitors iswith respect to personal wellness and/or physical exercise for purposesof fitness training, weight loss, or monitoring general health.Technological advances relating to optical sensors, signal processing,and display devices have made it possible to realize small, light-weightphysiological monitors that can be embodied as devices that may becomfortably worn by a user. Such wearable devices may include, forexample, wrist watches, bracelets, and arm bands.

Heart rate variability (“HRV”) is a physiological phenomenon offluctuations of time intervals between heartbeats. HRV can provideindications about the overall physical health of a person, includingwhether the person's body, such as their nervous system, is stressed orrelaxed. HRV can also provide a baseline score against whichfluctuations are compared, to determine whether a person's body isundergoing a healthy (or unhealthy) amount of physical activity.

The human autonomic nervous system is a part of the peripheral nervoussystem that supplies smooth muscle and glands, influencing the functionof internal organs such as the heart, stomach, and intestines. Thissystem functions primarily unconsciously. The autonomic nervous systemincludes two subparts: the sympathetic nervous system and theparasympathetic nervous system. The sympathetic nervous system isconsidered the “fight-or-flight” system while the parasympatheticnervous system is considered the “rest and digest” system. The twosystems have opposite actions in that one activates a physiologicalresponse while the other inhibits it. By determining the state of aperson's autonomic nervous system, it is possible to infer informationabout the user's overall state of physiologic stress. For example, if aperson's nervous system is in a sympathetic state during a period whenthe body should be relaxed, it can indicate stress or a lack ofphysiologic well-being.

Applicant has found that, using HRV, it is possible to determine whethera person's body is in a predominantly sympathetic or parasympatheticstate. This information can be used to inform the user more accuratelyabout their physiologic state and provide actionable information orrecommendations to the user for improving their health.

SUMMARY OF THE DISCLOSURE

The system can continuously test physiological parameters of a user,including periodic recording of heart rate variability, to assess stresslevel and physical fitness of users. The assessment may be carried outautomatically during user's usual daily physical activity. Based on theassessment, various actions can be taken.

The system may accomplish the assessment by using a wearable device suchas a bracelet, watch, arm band, chest strap, or any other wearabledevice, to collect data from different sensors (movements, time,illumination, atmosphere pressure and any others). Based on the sensordata, a processor in the system (on the wrist-word device or on aserver) can identify patterns of user's activity, such as sitting,walking, jogging, driving, television watching and others. Thewrist-worn device and/or server may look for the known patterns in realtime. If a pattern appears and is recognized, the wrist-worn device mayswitch on HRV recording. A server may compare HRV data of the samepatterns over time and recognize declining or improving stress level andphysical fitness levels in the individual. Although the user's activitycan be taken into account when interpreting physiological data, heartrate variability can also be determined without regard to the user'sactivity.

In one embodiment, the system includes a wearable device that obtainsreal-time physiological data and activity data from a user and transmitsthat data to another device. For example, the wearable device cantransmit the data to a computing device such as a cell phone, laptopcomputer, desktop computer, tablet, a remote server, or a combinationthereof. A computing device having a processor can perform variousstages for interpreting the physiological data and activity data.

For example, the processor can first receive the physiological data andthe activity data. It can then calculate a first HRV score for the userbased on physiological data from first time period, such as an averageHRV score for the previous week. The processor can also calculate asecond HRV score for the user based physiological data from a secondtime period, such as a moving average HRV score for the last seven days.Other time periods can be used as well, such as the previous day, week,or month.

The processor can present the user with at least one of the first andsecond HRV scores. In some examples, the user is presented with a scorebased on the first and/or second HRV scores but is different from theHRV scores. In other examples, the user is presented with the first andsecond HRV scores, as well as additional HRV scores based on additionalperiods of time. The score can be provided on a graphical user interface(“GUI”) of the wearable device or another device, such as the user'sphone. In one example, a graphical display is provided on a GUI thatincludes indicators for each day of the week. In response to a userselecting an indicator for a day of the week, the GUI can display an HRVscore for that selected day, including a trailing 7-day average scorefor that day.

The processor can perform further calculations on the HRV scores, suchas calculating a magnitude of a change in HRV scores between the firstHRV score and second HRV score. The processor can compare the calculatedchange to a threshold value, and if the calculated change is larger thanthe threshold value, take an action in response.

Taking an action in response can include a variety of different actions.In one example, the action is providing an alert to the user, such as bysending a notification to the user's phone or wearable device. Inanother example, the action includes suggesting an action or activityfor the user to engage in. This can include suggesting a specific typeof exercise for the user to undertake.

In another example, a system is provided that includes a wearable devicethat obtains real-time physiological data, including HRV data, from auser, obtains activity data from a user, and transmits the real-timephysiological data and activity data. A computing device having aprocessor can perform various stages for interpreting the physiologicaldata and activity data.

The computing device can receive the HRV and activity data anddetermine, based on at least the HRV data and activity data, whether theuser's autonomic nervous system is in a predominantly sympathetic orparasympathetic state. For example, the determination can includecomparing an average variance in a portion of the HRV data with athreshold value. The threshold value can be, for example, 5 seconds, 10seconds, or any other value. In response to determining that the user'sautonomic nervous system is in a sympathetic state, the device canperform an action.

Performing an action can include a variety of different actions. Forexample, it can include providing an alert to the user indicating thatthe user is under stress, recommending an activity for the user toperform, dimming or turning off a light, or playing or adjusting thevolume of music.

The above and further objects, features and advantages thereof will berecognized by those skilled in the pertinent art from the followingdetailed description of the invention when taken in conjunction with theaccompanying drawings. It is to be understood that both the foregoinggeneral description and the following detailed description areillustrative and explanatory only and are not restrictive of the claims.

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments and togetherwith the description, serve to explain the principles of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example image of a jogger wearing a device for obtainingand transmitting real-time physiological data in accordance with anembodiment.

FIG. 2 is an exemplary flow chart in accordance with an embodiment.

FIG. 3 is an exemplary flow chart in accordance with an embodiment.

FIG. 4 is an exemplary illustration of a system in accordance with anembodiment.

FIG. 5 is an illustration of an example graphical user interface inaccordance with an embodiment.

FIG. 6 is an exemplary illustration of a system in accordance with anembodiment.

FIG. 7 is an exemplary illustration of system components in accordancewith an embodiment.

FIG. 8 is an exemplary flow chart in accordance with an embodiment.

FIG. 9 is an exemplary flow chart in accordance with an embodiment.

DESCRIPTION OF THE EMBODIMENTS

Disclosed herein are embodiments of an apparatus for sensing, measuring,and displaying physiological information. In one embodiment, the systemmay continuously test physiological parameters of a user, includingperiodic recording of HRV, to assess stress level and physical fitnessof users. The assessment may be carried out automatically during user'susual daily physical activity.

The system may accomplish the assessment by using a wearable device suchas a bracelet, watch, arm band, chest strap, or any other wearabledevice to collect data from various sensors. The sensor data can includeboth physiological data (such as heart rate or blood oxygen level, forexample) and activity data (such as accelerometer-based movements, time,illumination, atmospheric pressure, and any others). Based on the sensordata, a processor in the system (either on the wearable device, a pairedcomputing device, or a remote server) can identify patterns of user'sactivity, such as sitting, walking, jogging, driving, televisionwatching and others. The processor can then calculate HRV by taking intoaccount the user's activity when parsing the physiological data.

In one example, the wearable device may include one or more opticalsensors incorporated into the device. The optical sensor(s) may beincorporated at a location of the wearable device such that, in use, asurface of the optical sensor may be adjacent or in close proximity to atargeted area of a user's body. In one embodiment, the optical sensor(s)may comprise one or more light sources for emitting light proximate thetargeted area and one or more optical detectors for detecting reflectedlight from the targeted area.

In one embodiment, the physiological information may be heart rateinformation. In other embodiments, the physiological information may beblood pressure information. Alternatively, the physiological informationmay be any information associated with a physiological parameter derivedfrom information received by the wearable device. Regardless, thephysiological information may be used in the context of, for example,athletic training, physical rehabilitation, patient monitoring, and/orgeneral wellness monitoring. Of course, these examples are onlyillustrative of the possibilities and the device described herein may beused in any suitable context.

While some of the example systems and devices described herein may bedepicted as wrist-worn devices, one skilled in the art will appreciatethat the systems and methods described below can be implemented in othercontexts, including the sensing, measuring, and display of physiologicaldata gathered from a device worn at any suitable portion of a user'sbody, including but not limited to, other portions of the arm, otherextremities, the head, and/or the chest.

The wearable device, or any number of additional devices, can be used tocollect physiological data from an individual. One such device is apulse oximetry device. Pulse oximetry is used to determine the oxygensaturation of arterial blood. Pulse oximeter devices typically containtwo light emitting diodes: one in the red band of light (660 nanometers)and one in the infrared band of light (940 nanometers). Oxyhemoglobinabsorbs infrared light while deoxyhemoglobin absorbs visible red light.Pulse oximeter devices also contain sensors that detect the ratio ofred/infrared absorption several hundred times per second. A preferredalgorithm for calculating the absorption is derived from theBeer-Lambert Law, which determines the transmitted light from theincident light multiplied by the exponential of the negative of theproduct of the distance through the medium, the concentration of thesolute and the extinction coefficient of the solute.

Reference will now be made in detail to certain illustrativeembodiments, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like items.

FIG. 1 illustrates a runner 102 wearing a physiological monitoringdevice 67 which obtains real-time physiological data for the runner, andprocesses stores that data on the device 67 or a separate device. Thedata can then be sent via transmission 902 over a network to a webportal for analysis in an embodiment. The runner subsequently receives acommunication based on the analyzed physiological data in the form of anadvertisement or personalized content.

FIG. 2 is an example flow chart of a method utilized in one embodiment.Step 210 can include obtaining physiological data and activity data atthe wearable device 67. Several different devices may be used to obtainphysiological data. For example, to obtain heart rate, pulse oximeters,pseudo-pulse oximeters, EKG devices, and other known devices may beused. Dickinson, U.S. Pat. No. 6,675,041, for an Electronic ApparatusAnd Method For Monitoring Net Calorie Intake, discloses such a deviceand is hereby incorporated by reference in its entirety. Exemplarymethods of obtaining physiological data useful with the presentinvention are disclosed in U.S. Patent Publication Number 2005/0251056,U.S. Patent Publication Number 2005/0251055, U.S. Patent PublicationNumber 2005/0251054, U.S. Patent Publication Number 2005/0251057, U.S.Patent Publication Number 2005/0251051, U.S. Patent Publication Number2005/0251424, all of which are hereby incorporated by reference in theirentireties.

Similar devices can be used to obtain activity data. Activity data caninclude accelerometer-based movement information, GPS-based locationinformation, time and duration, illumination, atmospheric pressure,temperature, step information, running speed and distance, and any otherdata describing the physical state of the user, the wearable device 67,or the environment.

In one example, the physiological and activity data are obtained andstored in a memory store of the wearable device 67 and later uploaded toa computing device such as a phone, tablet, laptop or desktop computer,or a remote server. In another example, the physiological and activitydata are obtained and immediately transmitted to the computing device.

At step 220 a processor of the computing device receives thephysiological data and activity data from the wearable device 67. Thedata can be obtained via any type of electronic communication, such aswireless transmission according to a wireless protocol such as WIFI orBLUETOOTH.

Having received the physiological data and activity data, at step 230the processor can calculate a first HRV score based on physiologicaldata from a first time period. For example, the processor can calculatean HRV score based on an hour-long period of time when the user is in aresting state. In another example, the first HRV score can be an averagescore based on multiple time periods and/or multiple days. For example,the first HRV score can be based on the previous week.

The wearable device 67 can sample across discrete periods of time, suchas 120 seconds. Any other period of time can also be used. In someexamples, the wearable device 67 only samples during periods of timewhen the user is in a resting state. In other examples, the wearabledevice 67 samples during a period of time that is later correlated to anactivity that the user was engaged in at the time. The user's activitycan be determined by, for example, processing the real-time activitydata of the user. For example, if the user's heartbeat is near theiraverage resting heartbeat and the accelerometer on the wearable deviceindicates that the user is not walking, the processor can determine thatthe user is stationary. Additional methods for interpreting the activitydata are discussed with respect to FIGS. 8 and 9.

An HRV score can be calculated by measuring the beat-to-beat intervalsof a user's heartbeat and performing further calculations based on thoseintervals. For example, the additional calculations can include a rootmean square of successive differences, or RMSSD, which is the squareroot of the mean of the squares of the successive differences betweenadjacent beat-to-beat intervals of the user's heartbeat. In one example,an HRV score is calculated using the following equation:R=(Ln(RMSSD)−2)*33.3  (1)

Where R is the result, or HRV score and RMSSD is the square root of themean of the squares of the successive differences between adjacentbeat-to-beat intervals of the user's heartbeat, where RMSSD is collectedduring a period of time such as a 24-period.

In some examples, an HRV score can be based on Equation 1, providedabove, and then weighted based on the user's age. A K index can beprovided for each age and can be used to weight the HRV scoreappropriately based on the user's age. For example, a 55-year-old usercan be associated with a K index of about 1.85. To calculate theweighted HRV score, the result from Equation 1 would be multiplied by1.85. Throughout the discussion of this disclosure, the term “HRV score”is meant to encompass an HRV score obtained by (1) using Equation 1, (2)using Equation 1 with a K index for weighting, (3) using anotherequation based on RMSSD, or (4) using any other equation that takes intoaccount the successive differences between adjacent beat-to-beatintervals of the user's heartbeat.

At step 240 the processor of the computing device can calculate a secondHRV score based on physiological data from a second time period. Forexample, the second HRV score can be a moving average score based on theprevious seven days. The second HRV can also be an HRV score for asingle day. Any other time periods can be used, such as by calculating amoving average for the previous two, three, four, five, or six days, forexample.

At step 250, the processor of the computing device can calculate amagnitude of a difference between the first and second HRV scores. Forexample, for a first HRV score of 65 and a second HRV score of 61, thedifference would be four. Similarly, for a first HRV score of 44 and asecond HRV score of 54, the difference would be 10.

At step 260, the processor of the computing device can compare thecalculated difference to a threshold value. In one example, thethreshold value is 5. Any other value can be chosen for the thresholdvalue, but Applicant has determined that a change of at least 5 pointscan indicate a significant shift.

In some examples, this step can also include comparing one or more HRVscores directly to a threshold number. For example, a third HRV scorecan be calculated based on a recent time period, such as the previous 24hours. The third HRV score can be compared to one or more thresholdnumbers. For example, if the HRV scores are provided on a scale of0-100, then a lower threshold of 20 and an upper threshold of 80 can beused. If the third HRV score falls below the lower threshold or abovethe upper threshold, the system can take further action in accordancewith the remaining steps discussed below. The comparison of the HRVscore to upper and lower thresholds can also be done for the first andsecond HRV scores described above.

At step 270, if the processor determines that the calculated changebetween the first and second HRV scores exceeds the threshold value, itcan take an action in response. Any type of action can be taken. Forexample, taking an action can include displaying a graphic on a GUIassociated with the computing device, the wearable device 67, or anyother device associated with the user. Taking an action can also includeupdating or altering the graphic on a GUI, such as by applying aparticular color to a portion of the graphic or by updating the numberor numbers displayed by the graphic. For example, if the calculateddifference exceeds a threshold in a manner indicating an improvement inthe user's health, a green-colored graphic can be displayed. Similarly,if the difference exceeds a threshold in a manner indicating a declinein the user's health, a red color can be displayed on the GUI. Anexample GUI is described in more detail with respect to FIG. 5.

In addition to displaying information, other actions can be taken as aresult of the calculated difference between the first and second HRVscores being above a threshold. For example, if the HRV scores indicatethat the user's health has declined since the previous week, the systemcan provide the user with an alert. The alert can include aninformational notice presented to the user on a display of the wearabledevice 67 or an associated computing device. The alert can inform theuser of the reason why their health has declined. For example, if theuser's HRV score has dropped too low or risen too high, the system canprovide an alert informing the user of this fact. The alert can alsoinclude information regarding possible causes of the undesirable HRVscore, as well as proposing various methods for improving the HRV score.The alert can also includes sounds, vibrations, or any other functionavailable to the wearable device 67 or computing device of the user.

In an example where the user is provided with a recommended action totake to improve their HRV score, the recommendation can encompass anyfeasible action, activity, or lack thereof. For example, if an HRV scoreindicates that the user's body has undergone an excessive amount ofexercise-related physical stress, the recommendation can be to cease allstrenuous exercise for a period of time, such as 12 hours, 24 hours, or48 hours. Similarly, the user's wearable device 67 or computing devicecan inform the user that additional physical activity would benefit theuser. The system can suggest, for example, that the user go for a run orwalk later that day.

In some examples, a user's mobile device can take various actions toassist the user in remedying an HRV score that is too low or too high.For example, a mobile device can determine that the user is listening tomusic and decrease the volume of the music to reduce stress. In anotherexample, the mobile device can be paired to a wirelessly controlledlight fixture or set of fixtures, and can decrease the brightness of thelights in order to reduce stress. In yet another example, the mobiledevice can determine that the user is driving based on GPS locations andsuggest a convenient location for the user to stop and either rest orperform an exercise. In another example, the system can recommendalterations to the user's sleep habits, such as going to bed earlier orwaking up later.

An additional exemplary flow chart in accordance with another embodimentis illustrated in FIG. 3. Stage 310 can include obtaining physiologicaldata and activity data at the wearable device 67. In one example, thephysiological and activity data is obtained and stored in a memory storeof the wearable device 67 and later uploaded to a computing device suchas a phone, tablet, laptop or desktop computer, or a remote server. Inanother example, the physiological and activity data is obtained andimmediately transmitted to the computing device.

Stage 320 can include receiving the physiological data and activity dataat a processor. For example, a processor of the computing devicereceives the physiological data and activity data from the wearabledevice 67. The data can be obtained via any type of electroniccommunication, such as wireless transmission according to a wirelessprotocol such as WIFI or BLUETOOTH.

Stage 330 can include determining, based on at least the physiologicaldata and activity data, whether the user's autonomic nervous system isin a predominantly sympathetic or parasympathetic state. This can bedone by, for example, comparing an HRV score for the user against one ormore thresholds. In one example, multiple thresholds are used to definecategories associated with a general health assessment.

The categories for a healthy adult can be, for example: 0-19 (verypoor); 20-39 (poor), 40-59 (good), 60-79 (high), and 80-100 (cautionrequired). For children, the categories can be shifted up by about 20points. For elderly adults, the categories can be shifted down by about20 points. An HRV score that is too high indicates excessiveparasympathetic nervous system activity, indicating that the user's bodyis undergoing stress.

The system can determine that a user's autonomic nervous system is in apredominantly parasympathetic state if the HRV score falls into aparticular category or rises above a threshold. In one example, thesystem identifies a predominantly parasympathetic state when the user'sHRV score is in the “caution required” category explained above. For anadult, this category can correspond to a score above 80. In anotherexample, the system identifies a predominantly parasympathetic statewhen the user's HRV score is above a particular threshold, such as 70.Similarly, the system can identify that a user's nervous system is in apredominantly sympathetic state when the user's HRV score is in the“very poor” or “poor” categories, or when the HRV score is below aparticular threshold number, such as 30.

In some examples, the system can determine whether the user's autonomicnervous system is in a predominantly parasympathetic state by comparinga weighted HRV score to one or more thresholds. The weighted HRV scorecan be calculated by first calculating an HRV score and then multiplyingit by a weighting factor. The weighting factor can take into account anyrelevant information, such as the user's age, sex, physical fitnesslevel, and fitness history. For example, if the user is a child theweighting factor can be 0.8. In that example, an HRV score of 70 wouldresult in a weighted HRV score of 56. Similarly, for an older user, theweighting factor can be 1.2. In that example, an HRV score of 70 wouldresult in a weighted HRV score of 84. Any other weighting factors can beused based on any relevant criteria.

If the user's HRV score falls into a particular category or surpasses athreshold indicating that the user's autonomic nervous system is in aparasympathetic state, at stage 340, in response to that determinationthe system can perform an action. Performing an action can include, forexample, providing an alert to the user indicating that the user isunder stress. For example, an information alert can be displayed on theuser's wearable device 67 or a mobile device paired to the wearabledevice 67. The alert can be accompanied by sound and/or vibration in oneexample.

The alert can include information to help the user determine how and whytheir body is under stress. For example, the alert can include a textexplanation that the user's heartbeat signature indicates that they areexperience stress. The alert can include an information button that, ifselected by the user, provides a more detailed explanation of HRV,including how it is calculated and what it means. The alert can alsoinclude recommendations for the user, such as resting or performing aparticular activity.

In addition to, or instead of, providing an alert, the system canperform an action intended to help the user recover from theirparasympathetic state. For example, the system can recommend an activityfor the user to perform. The activity can include resting or refrainingfrom any strenuous physical activity, in one example. In another examplethe activity can include standing up, walking, running, or performingany other physical activity or exercise. In yet another example, theactivity can include dimming or turning off a light, adjusting thevolume of music or of a television, adjusting the temperature, or takingany other action that can influence the user's physical stress levels.

FIG. 4 is an illustration of one system 20 of communicating a user'sreal-time physiological data with the web portal and providing acommunication to the user. The system 20 includes a physiologicalmonitoring device 21 that obtains real-time physiological data for auser and sends a transmission 20 a to an antenna 222 of a communicationnetwork for communication 20 b to a server 23 for a web portal 24. Then,a communication is sent to the user for display on a computing device,such as mobile device 27. In another example, the device 21 can send thetransmission 20 a to the user's mobile device 27, which then transmitsthe information to the server 23.

FIG. 5 is an illustration of an example GUI 500 in accordance withvarious embodiments disclosed herein. The GUI 500 can be displayed on,for example, a wearable device of the user, a mobile device paired withthe wearable device, or any other computing device capable of receivingphysiological information obtained at the wearable device.

The GUI 500 includes an average HRV score 540 located in the center ofthe display. The average HRV score 540 can be an average based on anyrelevant period of time. In the example of FIG. 5, the average HRV score540 is an average based on the previous week (for example, the previousSunday-Saturday timespan. In other examples, the average HRV score 540can be a moving average based on the previous seven days. The averageHRV score 540 can be color-coded to provide the user with additionalinformation. For example, it can be color-coded in accordance with thecategories discussed above with respect to FIG. 3.

A user can select the circle associated with the average HRV score 540for further information. For example, in response to the user selectingthe average HRV score 540 on the GUI 500, the user can be presented witha detailed breakdown of the average HRV score 540. This can include, forexample, the period of time for which the score is being calculated, thehealth category associated with the score and its implications for theusers, as well as tips for improving the score going forward. Any otherrelevant information can be provided here as well.

The GUI 500 also includes graphics associated with each day of the week.Each of these graphics 510, or “slices” 510, can correspond to aparticular day. For example, the slice 510 identified in FIG. 5 cancorrespond to Monday, and can include a day identifier 520 positionednear or within the slice 510. Each slice 510 can also include a dailyHRV score 530. The daily HRV score 530 can be based on any relevant timeperiod. For example, it can be based on a 24-hour period associated withthe identified day. In another example where sampling happens onlyperiodically, the daily HRV score 530 can be an average of HRV scorescalculated based on the periodic sampling within a 24-hour period. Inyet another example, the daily HRV score 530 can be a score reflecting atrailing average, such as a 2-, 4-, or 7-day trailing average.

A user can select a slice 510 to learn more information related to thatslice 510. In response to the user selecting a slice 510, the GUI canpresent an additional display showing more detailed informationassociated with that slice 510. For example, the additional display canexplain the time period for which the daily HRV score 530 is obtained,the health implications of the score, and suggestions for improving thescore.

Although 7 slices 510 are shown in FIG. 5, any number can be displayedon the GUI 500. For example, the display can include 7 slices 510, withone for each day of the week and an additional one for a moving averagebased on a multiple-day span of time. Furthermore, although the scoresdisplayed on the GUI 500 are described as various types of HRV scores,weighted HRV scores can be used instead of, or in addition to, thesescores.

A more detailed system of the present invention is shown in FIG. 6. Thesystem 600 may operate the computing device 27 (such as a watch orphone) to transmit real-time physiological date from a user through awireless network 104 to a web portal 106 hosted on an Internet-basedserver 107. A secondary computer 112 accesses the web portal 106 throughthe Internet 109. A wireless gateway 105 connects to the wirelessnetwork 104 and receives and delivers data from and to the device 27.The wireless gateway 105 additionally connects to the server 107 thatincludes a database 103 and a data-processing component 108 for,respectively, storing and analyzing the data.

The server 107, for example, may include multiple computers, softwarepieces, and other signal-processing and switching equipment, such asrouters and digital signal processors. The wireless gateway 105preferably connects to the wireless network 104 using a TCP/IP-basedconnection, or with a dedicated, digital leased line (e.g., aframe-relay circuit or a digital line running an X.25 or otherprotocols). The server 107 also hosts the web portal 106 usingconventional computer hardware (e.g. computer servers for both adatabase and the web site) and software (e.g., web server and databasesoftware). Additionally, the server 107 includes a web servicesinterface 110 that transmits data using an XML-based web services linkto a secondary, web-based computer application 111. This application 111is preferably a data-management system operating at a health clinic.

In one embodiment, the system 20 is a SUN MICROSYSTEM workstation suchas the SPARCstation brand workstation manufactured by Sun Microsystemsof Mountain View, Calif. Note that the following discussion of variousembodiments discussed herein will refer specifically to a series ofroutines which are generated in a high-level programming language (e.g.,the PERL, JAVA, PYTHON, SMALLTALK interpretive and scripting languages)which is interpreted and/or executed in system 20 at run-time. Thesefurther are used in conjunction with the browser and server softwareavailable from NCSA, MOSAIC NETACAPE MICROSOFT and other SPYGLASSlicenses including the specification of the appearance of displays inHTML. One skilled in the art appreciates that the following methods andapparatus may be implemented in special purpose hardware devices, suchas discrete logic devices, large scale integrated circuits (LSI's),application-specific integrated circuits (ASIC's), or other specializedhardware. Other programming languages, C, BasicC, C++ and otherOperating systems such as Unix, Posix, and variations of Linux platformsmay be utilized.

Another embodiment Web Server platform comprises an IBM RISC System/6000computer running the AIX (Advanced Interactive Executive) OperatingSystem and a Web server program, such as Netscape Enterprise ServerVersion 2.0, that supports interface extensions. The platform alsoincludes a graphical user interface (GUI) for management andadministration. The various models of the RISC-based computers aredescribed in many publications of the IBM Corporation, for example, RISCSystem 6000, 7013 and 7016 POWERstation and POWERserver While the aboveplatform is useful, any other suitable hardware/operating system/Webserver combinations may be used. Accordingly, the web server descriptionhere has equal application to apparatus having similar components andfunctions.

FIG. 7 depicts an exemplary processor-based computing system 201representative of the type of computing system that may be present in orused within the computing device 27 or serve 107 in an embodiment. Thecomputing system 201 is exemplary only and does not exclude thepossibility of another processor- or controller-based system being usedin or with one of the aforementioned components.

In one aspect, system 201 may include one or more hardware and/orsoftware components configured to execute software programs, such assoftware for storing, processing, and analyzing data. For example,system 201 may include one or more hardware components such as, forexample, processor 205, a random access memory (RAM) module 210, aread-only memory (ROM) module 220, a storage system 230, a database 240,one or more input/output (I/O) modules 250, and an interface module 260.Alternatively and/or additionally, system 201 may include one or moresoftware components such as, for example, a non-transitorycomputer-readable medium including computer-executable instructions forperforming methods consistent with certain disclosed embodiments. It iscontemplated that one or more of the hardware components listed abovemay be implemented using software. For example, storage 230 may act asdigital memory that includes a software partition associated with one ormore other hardware components of system 201. System 201 may includeadditional, fewer, and/or different components than those listed above.It is understood that the components listed above are exemplary only andnot intended to be limiting.

Processor 205 may include one or more processors, each configured toexecute instructions and process data to perform one or more functionsassociated with system 201. The term “processor,” as generally usedherein, refers to any logic processing unit, such as one or more centralprocessing units (CPUs), digital signal processors (DSPs), applicationspecific integrated circuits (ASICs), field programmable gate arrays(FPGAs), and similar devices, such as a controller. As illustrated inFIG. 2A, processor 205 may be communicatively coupled to RAM 210, ROM220, storage 230, database 240, I/O module 250, and interface module260. Processor 205 may be configured to execute sequences of computerprogram instructions to perform various processes, which will bedescribed in detail below. The computer program instructions may beloaded into RAM for execution by processor 205.

RAM 210 and ROM 220 may each include one or more devices for storinginformation associated with an operation of system 201 and/or processor205. For example, ROM 220 may include a memory device configured toaccess and store information associated with system 201, includinginformation for identifying, initializing, and monitoring the operationof one or more components and subsystems of system 201. RAM 210 mayinclude a memory device for storing data associated with one or moreoperations of processor 205. For example, ROM 220 may load instructionsinto RAM 210 for execution by processor 205.

Storage 230 may include any type of storage device configured to storeinformation that processor 205 may need to perform processes consistentwith the disclosed embodiments.

Database 240 may include one or more software and/or hardware componentsthat cooperate to store, organize, sort, filter, and/or arrange dataused by system 201 and/or processor 205. For example, database 240 mayinclude information to that tracks physiological parameters, activitytypes and levels, and HRV for users based on embodiments herein.Alternatively, database 240 may store additional and/or differentinformation. Database 240 may also contain a plurality of databases thatare communicatively coupled to one another and/or processor 205, of mayconnect to further database over the network.

I/O module 250 may include one or more components configured tocommunicate information with a user associated with system 201. Forexample, I/O module 250 may include a console with an integratedkeyboard and mouse to allow a user to input parameters associated withsystem 201, such as the identification of the user to independentlytrack different users of the computing device (e.g., a watch shared bydifferent users). I/O module 250 may also include a display including agraphical user interface (GUI) for outputting information on a monitor.I/O module 250 may also include peripheral devices such as, for example,a printer for printing information associated with system 201, auser-accessible disk drive (e.g., a USB port, a floppy, CD-ROM, orDVD-ROM drive, etc.) to allow a user to input data stored on a portablemedia device, a microphone, a speaker system, or any other suitable typeof interface device.

Interface 260 may include one or more components configured to transmitand receive data via a communication network, such as the Internet, alocal area network, a workstation peer-to-peer network, a direct linknetwork, a wireless network, or any other suitable communicationplatform, such as Bluetooth. For example, interface 260 may include oneor more modulators, demodulators, multiplexers, demultiplexers, networkcommunication devices, wireless devices, antennas, modems, and any othertype of device configured to enable data communication via acommunication network.

Optical sensor 570 may allow the system 201 to determine if a user isinside or outside, or in a lighted versus darkened room. This may allowfor further insights into the user's activity in conjunction with othersensors that detect motion or physiological attributes.

In one embodiment, the system may continuous test physiologicalparameters of a user, including periodic recording of heart ratevariability, to assess stress level and physical fitness of users. Theassessment may be carried out automatically during user's usual dailyphysical activity.

FIGS. 8 and 9 include exemplary steps for continuously testing activitydata and the physiological parameters of a user. FIG. 8 includes examplesteps for identifying stationary states of a user and for collectingdata regarding body states of a user. At 802, exemplary steps related todetecting a stationary state are shown.

At step 810, various sensors in a computing device, such as a wrist-wornwatch or bracelet, may detect light, movement, noise, or otherenvironmental characteristics that may be used as sensed activity data.Sensors such as an optical sensor, piezoelectric sensor, accelerometer,and/or microphone, may make these detections. Other sensedcharacteristics may include temperature or temperature change. This mayindicate that a user has, for example, transitioned from outdoors toindoors or vice versa. Another characteristic may be light frequency,which may inform the type of environment the user is in. For example,the light frequencies of fluorescent lighting differ from those ofsunlight. Other data, such as the sound of typing, the sound of talking,or oxygen or blood flow levels consistent with various types of activitymay be recorded via the various sensors.

At step 815, other activity data representative of user activities orother attributes may be input by the user and/or retrieved by a server.For example, through inputting activity data, the user may indicate anactivity, such as running, cycling, swimming, hiking, or weight liftingis taking place by selecting an activity on their computing device.Other attributes, such as mood, may be input by the user, such as withrespect to a social media profile. Age may also be input by the user,for use in adjusting HRV insights made by the system, since age and HRVare correlated.

The user may also indicate a location where they are present thatprovides insight into their activities, such as a restaurant location, agym location, or a sporting event location. In one embodiment, thecomputing device uses GPS to collect this information. In still anotherembodiment, data regarding the apparel (e.g., clothes or shoes) of theuser may be collected through user input or though wireless connectionbetween the apparel and the computing device. The apparel worn mayindicate the user's activity based on differences in purpose of workapparel, sleep apparel, and exercise apparel.

At step 820, the data from steps 810 and 815 may be collected on thecomputing device and/or a server that communicates with the computingdevice. This may include storing the data in a database with respect toa particular user identifier.

At step 825, the server may make determinations regarding the user'sactivity by analyzing patterns in the collected data, such as throughcluster analysis-type data mining. Other techniques for machinelearning, pattern recognition, and bioinformatics may be incorporated inaddition or in the alternative. This may help remove or diminish theweight of outlier data and noise from the analysis. The cluster modelmay include algorithms based on both the sensor inputs of step 810 andthe user inputs of step 815.

The analysis may include a comparison against predefined types ofactivity that are stored in a database 832. The database 832 may includepredefined rules that allow the system to identify and recognizedifferent activity types. Different activities may include sitting,television watching, driving, jogging, sleeping, eating, working at adesk, gardening, lifting weights, and more.

In one embodiment, the database may be continuously updated by thesystem to learn new patterns indicative of particular states based onboth the step 810 and 815 data. The rules may be in flux based onuser-verified activities. For example, at step 830, the system maycollect new rules based on the determinations at step 825 and/or adjustexisting predefined rules in database 832 based on a user's particularhabits. For example, a particular user may have a proclivity for acertain activity type during a particular time period, so the rules maybe adjusted specifically for that user to bias towards the activity typeduring the detected time period.

From those data-defined activities, particular stationary activity maybe determined at step 830 in real time. The system may utilize rules at836 to compare real-time data against historical patterns in thedatabase at step 832. Various patterns and indicia may be analyzed todetermine the stationary activity. For example, as shown in step 834,stationary activity may be indicated when the same pose is kept andheart rate does not change abruptly. A consistent heart rate and bodylocation tend to indicate stationary activity.

This may be analyzed to determine the user is stationary based on one ormore time thresholds. For example, the heart rate and movement of a usermay be tracked over 60 seconds, 128 seconds, and 256 seconds in oneembodiment. The different time thresholds may allow for determiningdifferent types of stationary activity if the body state does not changeover those time thresholds. In another embodiment, recording of userheart rate and movement continues during an activity even after thatspecific activity has been recognized. This recorded data may beutilized to further develop the pattern recognition involved inidentifying the activity again in the future.

Other special types of activity and stationary activities may bedetermined at step 838. For example, a sharp change in body pose, suchas from laying down to standing up in a short time (e.g., less than fiveminutes) may be detected.

Additional exemplary body state data collection steps are shown at 848.At step 850, sensors and previously recognized and/or reported data maybe utilized by the system in analyzing body state.

At step 855, the system may recognize types of activity, includingstationary activity, in real time. If a particular state is recognizedat step 860, then HRV recording may be switched on at step 865. Othertypes of data may also be collected as step 865, such as blood pressure.Based on the recorded HRV and/or other collected data, a particularstate (such as stress level) may be verified and/or a determination maybe made regarding when the state is ending. Additionally, HRV datacollection may allow for analysis of quality of rest and othercharacteristics within a stationary state.

Continuing at FIG. 9, exemplary steps for calculating HRV indices areillustrated at 900. At step 902, the database is queried to obtain therecorded HRV data and reference the data to activity types and timingthresholds. The data may be analyzed at step 905 to determine if aspecial type of activity is present, such as a user sitting up orrolling over at 910.

If a special type of activity is not detected, then the system may applya first noise removing algorithm at 915. Then at step 920, the systemmay calculate HRV indicia (also referred to herein as HRV indexes).

Alternatively, if a special type of activity is detected, the system mayperform a different noise removing algorithm at step 930 and then, atstep 940, calculate HRV indicia separately according to when the bodyposition or pose was unchanged and after the body position or posechanged. The noise removing algorithm may be specific to the detectedtype of activity and/or analog sensor relied on because differentactivities may tend to introduce different types and/or levels of noise.Thus, recognizing an activity also may cause the system to apply a moresuitable noise algorithm, meaning that the number of noise algorithmsmay equal the number of activities in one embodiment.

These calculated HRV indicia are then further analyzed, such as throughthe exemplary steps shown at 945. For example, at step 950 HRV indiciafor multiple days may be analyzed to generate insights for the user. Asshown at 960, day by day statistics may include values representative ofhow much the user performs each specific activity type per day. The usermay be able to use an app, web interface, or display on a watch to seespecific activity statistics for each day in one embodiment. In anotherembodiment, the statistics may be presented as bar charts, and bars mayrepresent activity levels for each activity. Bar graphs for multipledays may be overlaid on top of one another to easily visualize changesper day. The activity levels may be given scores within the range of 0to 100 to give a user a numeric reference to strive towards.

In another embodiment, the user may be able to select a particularactivity and day and see specific details about that activity, such asheart rate, duration, location, and others.

At step 970, the system may also present conclusions to the userregarding the user's body state. For example, the system may determinethat the user's body state is good, strained, or poor in one embodiment.These insights may allow the user to better tailor activity regimens(e.g., exercise) such that their body state is not poor or strained.

In another embodiment, the conclusion indicates that the user has one ofa variety of physiological characteristics, including a high or lowblood pressure, high or low stress level, underweight or overweight,rapid heartbeat or arrhythmia, possibility of disease, or improvinghealth compared to other similar users.

At step 980, the system may present advertisements to the user inconjunction with the statistics and/or conclusions. For example, if theuser is having trouble sleeping, as determined by their sleep statebeing below an activity threshold, fluctuations of HRV during sleep,and/or sudden movements during sleep, an advertisement for sleepassistance product may be presented. Similarly, if the daily levels forexercising are low, the user may be presented with gym advertisements.If the activity level for running begins to increase but running causesa HRV to change more than a threshold amount over a time frame,advertisements directed to new runners may be displayed.

Other embodiments of the aforementioned systems and methods will beapparent to those skilled in the art from consideration of thespecification and practice of this disclosure. It is intended that thespecification and the aforementioned examples and embodiments beconsidered as illustrative only, with the true scope and spirit of thedisclosure being indicated by the following claims.

What is claimed is:
 1. A system for providing user insights based onheart rate variability (HRV), comprising: a wearable device that obtainsreal-time physiological data from a user, obtains activity data from auser, and transmits the real-time physiological data and activity data;and a processor configured to: receive the physiological data and theactivity data, wherein the physiological data is used to calculate theHRV, wherein the activity data describes at least two of a physicalstate of the user, the wearable device, or an environment of the user,and wherein the activity data comprises at least three of theaccelerometer-based movements of the user, illumination information,illumination change information, atmospheric pressure information,activity type, apparel worn by the user, running speed and distanceinformation, step information, temperature information, temperaturechange information, light frequency, sound information, or locationinformation; identify, using the activity data, a stationary state ofthe user; and in response to identifying the stationary state:determine, based on correlating the physiological data and the activitydata, whether the user's autonomic nervous system is in a predominantlyparasympathetic state; and in response to determining that the user'sautonomic nervous system is in a parasympathetic state, perform anaction.
 2. The system of claim 1, wherein determining whether the user'sautonomic nervous system is in a predominantly parasympathetic statecomprises calculating a heart rate variability score and comparing thescore to a threshold value.
 3. The system of claim 1, whereindetermining whether the user's autonomic nervous system is in apredominantly parasympathetic state comprises calculating a heart ratevariability score, weighting the score based on information associatedwith the user, and comparing the weighted score to a threshold value. 4.The system of claim 3, wherein the information associated with the userincludes at least one of the user's age, sex, physical fitness level,and fitness history.
 5. The system of claim 1, wherein performing anaction comprises providing an alert to the user indicating that the useris under stress.
 6. The system of claim 1, wherein performing an actioncomprises recommending an activity for the user to perform.
 7. Thesystem of claim 1, wherein performing an action comprises dimming orturning off a light.
 8. The system of claim 1, wherein performing anaction comprises playing music or adjusting the volume of music.
 9. Amethod for providing user insights based on heart rate variability,comprising: receiving physiological data and activity data obtained inreal time from a user; determining, based on the physiological data andthe activity data, whether the user's autonomic nervous system is in apredominantly parasympathetic state by steps comprising; identifying,using the activity data, a stationary state of the user using a bodystate of the user, wherein the stationary state is identified usingcriteria including identifying the stationary state when a same bodyposition or pose of the user is kept and a heart rate does not changeabruptly during a time threshold; and in response to not identifying anactivity associated with the stationary state, calculating a first heartrate variation indicium and a second heart rate variation indiciumseparately according to when the same body position or pose wasunchanged and after the body position or pose changed; and in responseto determining that the user's autonomic nervous system is in aparasympathetic state, performing an action.
 10. The method of claim 9,wherein determining whether the user's autonomic nervous system is in apredominantly parasympathetic state comprises calculating a heart ratevariability score and comparing the score to a threshold value.
 11. Themethod of claim 9, wherein determining whether the user's autonomicnervous system is in a predominantly parasympathetic state comprisescalculating a heart rate variability score, weighting the score based oninformation associated with the user, and comparing the weighted scoreto a threshold value.
 12. The method of claim 11, wherein theinformation associated with the user includes at least one of the user'sage, sex, physical fitness level, and fitness history.
 13. The method ofclaim 9, wherein performing an action comprises providing an alert tothe user indicating that the user is under stress.
 14. The method ofclaim 9, wherein performing an action comprises recommending an activityfor the user to perform.
 15. The method of claim 9, wherein performingan action comprises a first action of dimming or turning off a light ora second action of playing music or adjusting the volume of the music.16. The method of claim 9, further comprising: calculating a pluralityof heart rate variability scores using at least the physiological datafor a respective plurality of time periods; and displaying, on a deviceof the user, each of the plurality of the heart rate variability scoresin a respective slice of a doughnut-shaped graph, wherein the respectiveslices have equal widths, wherein the respective slices have respectiveheights indicative of respective magnitudes of the heart ratevariability scores, and wherein an average of the plurality of the heartrate variability scores is displayed at a center of the doughnut-shapedgraph.
 17. A non-transitory, computer-readable medium containinginstructions that, when executed by the processor of a computing device,cause the processor to carry out stages for providing user insightsbased on heart rate variability, the stages comprising: receivingphysiological data and activity data obtained in real time from a user,wherein the physiological data is used to calculate the HRV, wherein theactivity data describes at least two of a physical state of the user,the wearable device, or an environment of the user, and wherein theactivity data comprises at least three of the accelerometer-basedmovements of the user, illumination information, illumination changeinformation, atmospheric pressure information, activity type, apparelworn by the user, running speed and distance information, stepinformation, temperature information, temperature change information,light frequency, sound information, or location information; identify,using the activity data, a stationary state of the user; and in responseto identifying the stationary state: determine, based on correlating thephysiological data and the activity data, whether the user's autonomicnervous system is in a predominantly parasympathetic state; and inresponse to determining that the user's autonomic nervous system is in aparasympathetic state, perform an action.
 18. The non-transitory,computer-readable medium of claim 17, further comprising: determining,based on at least the physiological data, whether the user's autonomicnervous system is in a predominantly parasympathetic state, whereindetermining whether the user's autonomic nervous system is in apredominantly parasympathetic state comprises calculating a heart ratevariability score and comparing the score to a threshold value; and inresponse to determining that the user's autonomic system is in aparasympathetic state and to identifying that the user is driving:identifying based on the location of the user a proximal location tostop; and informing the user to stop at the location to perform anactivity, wherein the activity is at least one of resting or exercising.19. The non-transitory, computer-readable medium of claim 17, whereindetermining whether the user's autonomic nervous system is in apredominantly parasympathetic state comprises calculating a heart ratevariability score, weighting the score based on information associatedwith the user, and comparing the weighted score to a threshold value.20. The non-transitory, computer-readable medium of claim 19, whereinthe information associated with the user includes at least one of theuser's age, sex, physical fitness level, and fitness history.